Mean Square Research Articles

Feature selection using non-parametric correlations and important features on recursive feature elimination for stock price prediction

Stock price prediction using machine learning is a rapidly growing area of research. However, the large number of features that can be used can complicate the learning process. The feature selection method that can be used to overcome this problem is recursive feature elimination. Standard recursive feature elimination carries the risk of producing inaccurate algorithms because the top-ranked features are not necessarily the most important features. This research proposes a feature selection method that combines important features and nonparametric correlation in recursive feature elimination for stock price prediction. The data features used are technical indicators and stock price history. The recursive feature elimination method is modified with important features and nonparametric correlation features. The strategy for combining important features and non-parametric features is average weight, 25:75% weight, 75:25% weight, maximum weight, and minimum weight. The performance evaluation results show that the proposed feature selection method succeeded in obtaining small error values. The proposed method for predicting PT Bank Rakyat Indonesia Tbk (BBRI) stock prices obtains mean squared error, root mean square error, mean absolute error, and mean absolute percentage error evaluation values of 0.0000336, 0.00577, 0.00459, and 1.78%, respectively. This shows that recursive feature elimination with feature selection that combines important features and non-parametric correlation works better than the original recursive feature elimination at predicting stock prices.

Examining the Insight Impacts of Indian Ocean Pressure on Autumntime Precipitation Variability over Tasmania through Singular Value Decomposition Analysis

This study explores further the impacts of the relationship between autumntime mean sea level pressure (MSLP) over the Indian Ocean and autumntime precipitation over Tasmania. We employ a combination of cross-correlation analysis, Singular Value Decomposition (SVD), and covariance measures to unravel this intricate connection. Our results reveal that MSLP over the Indian Ocean significantly influences Tasmania’s autumn precipitation, particularly in the region extending from 125°E eastward and from 35°S southward. This MSLP-precipitation link is statistically significant, characterized by a Pearson’s coefficient of correlation of 0.63 (p < 0.05). In addition, we calculate the Root Mean Square Covariance (RMSC) to gauge the strength of this association. The RMSC value, calculated at 0.33, surpasses the threshold of 0.1, emphasizing a robust and significant connection between autumntime MSLP and precipitation in Tasmania. Our results emphasize that the first leading mode of SVD accounts for 84% of the variability in both MSLP over the Indian Ocean and precipitation in Tasmania. These results contribute to a more profound understanding of the factors governing autumn precipitation in Tasmania and offer valuable insights for meteorological and climate science.

Using Laser Point Scanning Thermography for Quality Monitoring of Products Made of Composite Materials

Introduction. Control of the presence of subsurface defects in products from composite materials is necessary for verification of products after release from production and in the process of operation. Aim of the Study. The purpose of the presented work is to estimate the parameters of subsurface defects using local laser thermography, suitable for quality control of both small objects and suspicious areas of large objects with curved surfaces. Materials and Methods. The laboratory setup on which this work was carried out includes a robotic arm, a COX CG640 thermal imager and a 3 W laser. The method was tested on a fiberglass sample with introduced delamination defect simulations located at different depths below the surface. By means of computer modeling rational parameters of thermographic control were selected, providing reliable detection of the defect at a depth of up to 3 mm under the surface of the composite sample. Results. Numerical modeling of surface temperature field induced by moving focused laser beam was carried out using COMSOL software package. It showed that laser beam with 3 W power moving at 5 mm/s provided the thermal contrast sufficient to detect the defects at the depth up to 3 mm. The obtained experimental data are in satisfactory agreement with numerical modeling both qualitatively and quantitatively. Experimental data were used to construct a regression model for determining defect depth based on the maximal thermal contrast and the time interval between heating and the contrast maximum. Discussion and Conclusion. The results obtained in this work allow us to propose a technique for detecting defects in fiberglass plastics and estimating their depth. The coefficient of determination for the obtained regression model was found to be equal to 0.95, and the mean square error of the metric was no more than 0.016 mm2. The use of a robotic arm to scan objects will make it possible to investigate objects with complex curved surfaces.

A Comparative Study for Privacy-Aware Recommendation Systems

Recommendation systems are sophisticated processes for information filtering designed to offer users tailored recommendations based on their preferences and interests. Users need help to choose between options as the amount of information on the web grows. As a result, it is critical to deliver personalized recommendations to consumers to promote user loyalty and satisfaction. Because recommender systems use sensitive user information such as ratings, comments, likes, and dislikes, this information can be leaked if no privacy measures are taken. As a result, we presented a comparison of privacy-aware recommendation systems in this paper. Two experiments are carried out. In the first experiment, we examined collaborative filtering algorithms on perturbed ratings and then compared hybrid, collaborative, and content-based algorithms on perturbed ratings in the second experiment. According to the results, the Singular Value Decomposition++ (SVDpp) algorithm presented the lowest Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values for epsilon 100, with 0.8889 and 0.6822, respectively. Furthermore, for epsilon 100, the hybrid filtering technique had the lowest RMSE and MAE rates of 0.90664 and 0.69813, respectively.

Exploring social networks through stochastic multilayer graph modeling

Several graph models are available today to model online social networks. These graph models are used to analyze the structural properties of the online social network, such as detecting communities, finding the influential spreader and predicting the behavior of the network. However, these models are based on deterministic single-layer graphs that may not be appropriate when online users use multiple social networks at the same time and social networks provide specific services. Moreover, because of the unknown and dynamic nature related to the behaviors and activities of online users, as well as structural and behavioral parameters, which may vary over time, stochastic multi-layer models could be applied to better capture and represent this phenomenon, as well as the dynamic nature of social networks. For example, in recommender systems, users' interests are unknown parameters and vary over time. Therefore, stochastic multilayer graph modeling can be used to develop recommender systems by considering different layers for different types of interests or preferences. In this paper, we propose a stochastic multilayer graph in which the edges are associated with random variables as a potential graph model for the analysis of online social networks. Therefore, after redefine some network measures related to stochastic multilayer graphs, we propose a Cellular Goore Game (CGG) based algorithm to computes the redefine network measures. A CGG-based algorithm computes defined network measures by learning automata from the edges of stochastic multilayer graphs. The experimental results show that the new CGG-based algorithm requires fewer samples from the edges of stochastic multilayer graphs than the standard sampling method in network measures calculation. Furthermore, the obtained results demonstrate that, from an evaluation perspective, the CGG-based algorithm provides superior results in terms of Kolmogorov-Smirnov (KS-test), Pearson Correlation Coefficient (PCC), Normalized Root Mean Square Error (NRMSE) and Kullback–Leibler divergence (KL-test).

Multi-objective optimization of the performance for a marine methanol-diesel dual-fuel engine

Marine methanol-diesel compound combustion (DMCC) engines have received extensive attention in the shipping industry. This work aims to further improve the performance and evaluate the carbon reduction potential of the marine DMCC engine. First, the computational fluid dynamic model of the dual-fuel engine was built. Then, an intelligent regression method based on the grey wolf optimizer (GWO) algorithm optimized support vector machine regression (SVR) was proposed. The methanol substitution rate (MSR), excess air ratio (EAR), and diesel injection timing (DIT) were adopted as the decision variables to establish the regression models for NOx, CO, and CO2 emissions and indicated specific fuel consumption (ISFC). Subsequently, a multi-objective grey wolf optimizer (MOGWO) and entropy weight TOPSIS were employed to obtain the optimal case for achieving low NOx and CO emissions and ISFC. Finally, the maximum MSR was explored to achieve greater carbon reduction benefits while balancing the NOx emission and ISFC. The results showed that after GWO optimization, the coefficients of determination of the SVR regression models are all greater than 0.95, with mean square errors consistently below 0.015. It indicates that the regression models have excellent consistency and applicability. The optimal case calculated by MOGWO and entropy-weighted TOPSIS enables co-optimization of the NOx emission and ISFC while maintaining the CO emission below 600 ppm. Compared to the original case, the optimal case achieves reductions of 26.22 % for NOx and 13.39 % for ISFC. The exploration of the maximum MSR reveals that in the Pareto optimal set, the maximum MSR reaches 56.16 %, at which point the NOx and CO2 emissions reduce by 75.85 % and 59.32 % respectively, while the ISFC increases by 21.67 % compared to the original case.

Prediction of jaw opening function after mandibular reconstruction using subject-specific musculoskeletal modelling.

Mandibular reconstruction patients often suffer abnormalities in the mandibular kinematics. In silico simulations, such as musculoskeletal modelling, can be used to predict post-operative mandibular kinematics. It is important to validate the mandibular musculoskeletal model and analyse the factors influencing its accuracy. To investigate the jaw opening-closing movements after mandibular reconstruction, as predicted by the subject-specific musculoskeletal model, and the factors influencing its accuracy. Ten mandibular reconstruction patients were enrolled in this study. Cone-beam computed tomography images, mandibular movements, and surface electromyogram signals were recorded preoperatively. A subject-specific mandibular musculoskeletal model was established to predict surgical outcomes using patient-averaged muscle parameter changes as model inputs. Jaw bone geometry was replaced by surgical planning results, and the muscle insertion sites were registered based on the non-rigid iterative closest point method. The predicted jaw kinematic data were validated based on 6-month post-operative measurements. Correlations between the prediction accuracy and patient characteristics (age, pathology and surgical scope) were further analysed. The root mean square error (RMSE) for lower incisor displacement was 31.4%, and the error for peak magnitude of jaw opening was 4.9 mm. Age, post-operative infection and radiotherapy influenced the prediction accuracy. The amount of masseter detachment showed little correlation with jaw opening. The mandibular musculoskeletal model successfully predicted short-range jaw opening functions after mandibular reconstruction. It provides a novel surgical planning method to predict the risk of developing trismus.

Mechanistic model of critical heat flux in rod bundles based on a high-precision subchannel code

A mechanistic model for predicting critical heat flux (CHF) in rod bundles is developed based on the high-precision subchannel code ATHAS. To account for the non-uniform distribution of quality in subchannels caused by the presence of mixing vane grids (MVGs) and guide tubes (GTs), further subdivision of conventional subchannels and the incorporation of MVG crossflow models are implemented in ATHAS to provide a more detailed local flow field for the CHF model. The CHF mechanism model is developed based on the concepts of bubble crowding and assumption of local phenomenon. An equivalent-tube concept is proposed to convert the turbulent intensity distribution on the tube surface to that on the rod bundle surface. The present model is implemented in ATHAS and evaluated against CHF experimental data of rod bundles under the conditions of interest for pressurized water reactors (PWRs). The predicted results show good agreement with the experimental CHF data, with an average error and root mean square of 0.99% and 4.69%, respectively, for a total of 601 data points. Furthermore, owing to the use of the subchannel subdivision method, the present model does not require any cold wall effect correction and can accurately predict the radial position of CHF.

Cluster-Based Cross Layer-Cross Domain Routing Model with DNN-Based Energy Prediction

In WSN, extending the network life is still a major problem that needs to be tackled. Cross-layer protocols are used to get around these problems. In this paper, a new cross-layer design routing model is presented using a clustering-based technique. The proposed model is proceeding with the optimal cluster-based routing model via a new algorithm. Initially during the network generation, the node’s energy is predicted by the deep learning model termed as DNN model on the basis of the distance between node and sink as the input. Subsequently, during the clustering process, the cluster head is optimally selected via a new optimisation algorithm named Self-Improved Shuffle Shepherd Optimisation (SISSO) Algorithm. The cluster head selection is done by considering the constraints including Link quality, Distance, Overhead, Energy and Delay. Finally, a Modified Kernel Least Mean Square (MKLMS)-based data aggregation process is to eliminate the redundant data transmission. The performance of the SISSO method is proven superior over other conventional approaches with regard to the alive node and network lifetime. In the alive node analysis of supernode, the proposed SISSO model achieves the maximal number of alive supernodes at 2,000 rounds (i.e. 0.67) than other conventional methods.

Inversion of Boreal Forest Height Using the CRITIC Weighted Least Squares Three-Stage Temporal Decorrelation Iterative Algorithm

The inversion of forest height using the RVoG (Random Volume over Ground) model is susceptible to overestimation or underestimation due to three error sources, propagating inaccuracies to the estimated forest height. Furthermore, variations in the height and density of the scenario could impact how well the RVoG three-stage inversion performs. This work utilizes the L-band single-baseline full polarization interferometric dataset as its basis. It optimally applies the CRITIC (Criteria Importance Through Intercriteria Correlation) method to the first stage of a three-stage process. This approach aims to overcome the issues mentioned above and enhance the accuracy of forest parameter estimation. A CRITIC weighted least squares temporal decoherence iterative algorithm is also proposed for the characteristics of the spaceborne data, in combination with the temporal decoherence algorithm of previous research. The proposed approach is tested and applied to both simulated and actual data. The optimization approach is first assessed using four simulated datasets that simulate coniferous forests with different densities and heights. The preliminary findings suggest that optimizing the complex coherence fitting process through the weighted least squares method enhances the accuracy of ground phase estimation and, consequently, improves the accuracy of the three-stage approach for inverting forest height. The ground phase estimation results for low forest height consistently remained within 0.02 rad, with a root mean square error (RMSE) below 0.05 rad, and no saturation occurred with increasing forest density. The enhanced algorithm outperforms the traditional technique in terms of accuracy in ground phase estimation. Subsequently, the optimized approach is applied to ALOS-2 spaceborne data, proving more successful than the conventional algorithm in reducing the RMSE of forest height. The findings illustrate the method’s superior inversion performance, obtaining an accuracy exceeding 80% in both the test and validation sets. The validation set’s RMSE is approximately 2.5 m, and the mean absolute error (MAE) is within 2 m. Moreover, it is observed that to counteract the uncertainty in temporal decoherence induced by climate change, a larger temporal baseline necessitates a larger random motion compensation term and phase offset term.

Drug Solubility Prediction: A Comparative Analysis of GNN, MLP, and Traditional Machine Learning Algorithms

The effective development and design of pharmaceuticals hold fundamental importance in the fields of medicine and the pharmaceutical industry. In this process, the accurate prediction of drug molecule solubility is a critical factor influencing the bioavailability, pharmacokinetics, and toxicity of drugs. Traditionally, mathematical equations based on chemical and physical properties have been used for drug solubility prediction. However, in recent years, with the advancement of artificial intelligence and machine learning techniques, new approaches have been developed in this field. This study evaluated different modeling approaches consisting of Graph Neural Networks (GNN), Multilayer Perceptron (MLP), and traditional Machine Learning (ML) algorithms. The Random Forest (RF) model stands out as the optimal performer, manifesting superior efficacy through the attainment of minimal error rates. It attains a Root Mean Square Error (RMSE) value of 1.2145, a Mean Absolute Error (MAE) value of 0.9221, and an R-squared (R2) value of 0.6575. In contrast, GNN model displays comparatively suboptimal performance, as evidenced by an RMSE value of 1.8389, an MAE value of 1.4684, and an R2 value of 0.2147. These values suggest that the predictions of this model contain higher errors compared to other models, and its explanatory power is lower. These findings highlight the performance differences among different modeling approaches in drug solubility prediction. The RF model is shown to be more effective than other methods, while the GNN model performs less effectively. This information provides valuable insights into which model should be preferred in pharmaceutical design and development processes.

Development and validation of the perceived symptom manageability scale among people living with the human immunodeficiency virus

Background“Perceived Symptom Manageability (PSM)” is essential in symptom management among people living with HIV. As a standardized assessment instrument was lacking, we developed a PSM scale for people living with human immunodeficiency virus (PSM-HIV).MethodsData analysis was performed using the sample from HIV-designated medical institutions (N = 540). Psychometric testing, namely reliability and validity, is assessed by unidimensionality, internal consistency, exploratory and confirmatory factor analysis, and structural equation modeling.ResultsThe final version of the PSM- HIV scale contained 15 items. This scale was submitted to a principal components analysis with varimax rotation, and three factors were obtained, explained by a total variance of 63.10%. The three factors were named Cognitive-Behavioral, Affective Interaction, and Self-Attitude. The results show that the scale had high reliability, Cronbach’s α of the scale ranged from 0.71 to 0.92, and the Intraclass Correlation Coefficient was 0.88. The structural equation model supports a factor model with the acceptable fit (χ2/df (CMIN/DF) = 2.50, Root Mean square Residual (RMR) = 0.03, Goodness-of-Fit Index (GFI) = 0.93, Adjusted Goodness of Fit Index (AGFI) = 0.90, Normed Fit Index (NFI) = 0.93, Incremental Fit Index (IFI) = 0.96, Comparative Fit Index (CFI) = 0.96). The average variance extracted was 0.38 ∼ 0.59, and the composite reliability was 0.70 ∼ 0.91, indicating that the convergent validity of the scale is acceptable. Subjects with different stages of the disease reached significance(χ2 = 9.02; df = 2, P<0.05), meaning moderate Known-Groups Comparison Validation.ConclusionsThe PSM-HIV scale is a valid instrument that measures overall attitude and belief about controlling or coping with HIV-relevant symptoms.

G-K BertDTA: A graph representation learning and semantic embedding-based framework for drug-target affinity prediction

Developing new drugs is costly, time-consuming, and risky. Drug-target affinity (DTA), indicating the binding capability between drugs and target proteins, is a crucial indicator for drug development. Accurately predicting interaction strength between new drug-target pairs by analyzing previous experiments aids in screening potential drug molecules, repurposing them, and developing safe and effective medicines. Existing computational models for DTA prediction rely on strings or single-graph neural networks, lacking consideration of protein structure and molecular semantic information, leading to limited accuracy. Our experiments demonstrate that string-based methods may overlook protein conformations, causing a high root mean square error (RMSE) of 3.584 in affinity due to a lack of spatial context. Single graph networks also underperform on topology features, with a 6% lower confidence interval (CI) for activity classification. Absent semantic information also limits generalization across diverse compounds, resulting in 18% increment in RMSE and 5% in misclassifications within quantifications study, restricting potential drug discovery. To address these limitations, we propose G-K BertDTA, a novel framework for accurate DTA prediction incorporating protein features, molecular semantic features, and molecular structural information. In this proposed model, we represent drugs as graphs, with a GIN employed to learn the molecular topological information. For the extraction of protein structural features, we utilize a DenseNet architecture. A knowledge-based BERT semantic model is incorporated to obtain rich pre-trained semantic embeddings, thereby enhancing the feature information. We extensively evaluated our proposed approach on the publicly available benchmark datasets (i.e., KIBA and Davis), and experimental results demonstrate the promising performance of our method, which consistently outperforms previous state-of-the-art approaches. Code is available at https://github.com/AmbitYuki/G-K-BertDTA.

Robust equivalent circuit model parameters identification scheme for State of Charge (SOC) estimation based on maximum correntropy criterion

Equivalent circuit model (ECM) parameters identification, which aims to identify the model parameters of ECM accurately is vital for assessing battery status and refining battery management systems (BMS), extensive research has been conducted in this field. However, most of the existing studies rely on global similarity criteria such as Root Mean Square Error (RMSE) and Sum of Squares due to Error (SSE), which are highly sensitive to non-Gaussian noise, so they may not perform effectively when faced with the non-Gaussian noise which is often encountered in battery working environments. This article presents a robust ECM parameter identification scheme termed the Maximum Correntropy Criterion-based Gradient Ascending scheme (MCCGA). The proposed MCCGA scheme adopts correntropy for similarity assessment and leverages a gradient ascent algorithm to optimize the model parameters iteratively. Through these methods, the MCCGA scheme not only identifies model parameters with precision but also remains robustness to non-Gaussian noise. Extensive experimental results based on public dataset are provided, which validate the effectiveness, robustness and convergence of the proposed MCCGA scheme.

Tensile Failure Characterization of Glass/Epoxy Composites using Acoustic Emission RMS Data

The acoustic emission monitoring with artificial neural networks predicts the ultimate strength of glass/epoxy composite laminates using Acoustic Emission Data. The ultimate loads of all the specimens were used to characterise the emission of hits during failure modes. The six layered glass fiber laminates were prepared (in woven mat form) with epoxy as the binding medium by hand lay-up technique. At room temperature, with a pressure of 30 kg/cm2, the laminates were cured. The laminates of standard dimensions as per ASTM D3039 for the tensile test were cut from the lamina. The Acoustic Emission (AE) test was conducted on these specimens under the load of uni-axial tension in the 10 Ton capacity Universal Testing Machine (UTM). In the monitoring process, acoustic emission parameters such as hits, counts, energy, duration, Root Mean Square (RMS) Value and amplitude were recorded. The RMS Values corresponding to the amplitude ranges from tensile test were used to characterise the failure load of all the similar glass-epoxy composite specimens.

Field-Deployed Spectroscopy from 350 to 2500 nm: A Promising Technique for Early Identification of Powdery Mildew Disease (Erysiphe necator) in Vineyards

This study explores spectroscopy in the 350 to 2500 nm range for detecting powdery mildew (Erysiphe necator) in grapevine leaves, crucial for precision agriculture and sustainable vineyard management. In a controlled experimental vineyard setting, the spectral reflectance on leaves with varying infestation levels was measured using a FieldSpec 4 spectroradiometer during July and September. A detailed assessment was conducted following the guidelines recommended by the European and Mediterranean Plant Protection Organization (EPPO) to quantify the level of infestation; categorising leaves into five distinct grades based on the percentage of leaf surface area affected. Subsequently, spectral data were collected using a contact probe with a tungsten halogen bulb connected to the spectroradiometer, taking three measurements across different areas of each leaf. Partial Least Squares Regression (PLSR) analysis yielded coefficients of determination R2 = 0.74 and 0.71, and Root Mean Square Errors (RMSEs) of 12.1% and 12.9% for calibration and validation datasets, indicating high accuracy for early disease detection. Significant spectral differences were noted between healthy and infected leaves, especially around 450 nm and 700 nm for visible light, and 1050 nm, 1425 nm, 1650 nm, and 2250 nm for the near-infrared spectrum, likely due to tissue damage, chlorophyll degradation and water loss. Finally, the Powdery Mildew Vegetation Index (PMVI) was introduced, calculated as PMVI = (R755 − R675)/(R755 + R675), where R755 and R675 are the reflectances at 755 nm (NIR) and 675 nm (red), effectively estimating disease severity (R2 = 0.7). The study demonstrates that spectroscopy, combined with PMVI, provides a reliable, non-invasive method for managing powdery mildew and promoting healthier vineyards through precision agriculture practices.

Controlling capillary flow by using laser-fabricated acrylic substrates with rectangular grooves for the improvement of repeatability of laser induced breakdown spectroscopy liquid analysis

To enhance the consistency of Laser-Induced Breakdown Spectroscopy (LIBS) in detecting dried liquid droplets, a rectangular groove substrate prepared by laser etching of a polymethyl methacrylate (PMMA) acrylic board is proposed in this work to suppress the Coffee Ring Effect (CRE). Located at the corner of the substrate, the right-angled heat source structure induces a significant inward thermal capillary flow within the groove, thus inhibiting solute capillary flow. At the later stages of heating, the edge deposition that occurs during evaporation is suppressed by the sawtooth-like microscopic grooves, along with the capillary effects in associated with the liquid. The rationale of CRE suppression by the rectangular groove substrate is theoredically analyzed based on the coupled thermal-solute capillary flow theory. With LIBS used to scan the samples within rectangular and circular groove substrates, it is revealed that the relative standard deviation (RSD) of spectral intensities for Cd I 259.28 nm and Cu I 324.27 nm in the rectangular groove substrate with a depth of 0.1 mm (RSD = 4.2%, RSD = 2.1%) is less significant than in the circular groove substrate (RSD = 41.2%, RSD = 39.3%). On average, the RSD is 7.6% and 7.3%, respectively, which indicates the effect of the rectangular groove substrate on CRE suppression. On this basis, calibration curves are drawn for Cd and Cu to indicate the good fitting results for the rectangular groove substrate, with the determination coefficient (R2) reaching 0.997 and 0.996 for Cd and Cu, respectively. The limit of detection (LOD) is 0.48 μg/mL and 0.35 μg/mL, respectively. The cross-validation root mean square error (RMSECV) is 2.02 μg/mL and 1.36 μg/mL, respectively. These results demonstrate that the rectangular groove substrate is effective in suppressing the CRE, and in enhancing the sensitivity of detection and the accuracy of heavy metal prediction, which provides guidance for platform selection in the pre-processing stage of LIBS technology.

Geographical coverage analysis and usage suggestions of temporal averaged aerosol optical depth product from GOES-R satellite data

ABSTRACT Geostationary satellites have the capability to offer AOD products with a higher frequency of observation within a given period, thus improving the geographical coverage of averaged AOD products compared to polar satellites. Moreover, the averaged AOD of geostationary satellites is more reflective of the average aerosol load as compared to AOD products derived from polar orbit satellites. Despite this, there is still an absence of comprehensive research on the comparative representativeness of AOD mean products from geostationary and polar orbit satellites, and most current research only focuses on retrieval accuracy. This paper compares the geographical coverage of averaged AOD products released by the Advanced Baseline Imager (ABI) sensor on the Geostationary Operational Environmental Satellite-R (GOES-R) (09:00 UTC-23:00 UTC) with AOD products released by the Moderate-resolution Imaging Spectroradiometer (MODIS) (16:00 UTC and 19:00 UTC) on hourly, daily, semi-monthly and monthly scales. Moreover, the Aerosol Robotic Network datasets were used to evaluate the accuracy of ABI AOD and to propose usage suggestions. In terms of daily AOD products, the AOD daily mean generated by the geostationary satellite GOES-R/ABI consistently outperforms the AOD daily mean generated by MODIS AOD in terms of spatial coverage. However, on monthly scales, the difference is no longer significant. With regard to accuracy, it is proved that when the time scale of averaging is gradually expanded, root mean square error (RMSE) and mean absolute error (MAE) gradually decrease. On the season scale, ABI AOD exhibits the highest level of accuracy during the autumn season (September, October, November); on the spatial scale, ABI AOD exhibits the best accuracy in North America. Therefore, if the daily, semi-monthly or monthly averaged ABI AOD datasets are used, the recommended time is autumn, and the study area is North America.

Research on dynamic characteristics of railway side-cracked slab for train-track coupled system

The Ballastless track have been extensively implemented in high-speed railways. However, during the long-term operational process, degradation, such as side crack or defect phenomena produced on the slab inevitably, poses a threat to the stability and the durability of the track structure. Consequently, it is imperative to propose a dynamic approach that can effectively predict the impact of side-cracked slab (SCS) on the vehicle-track coupled system. This paper utilizes co-simulation technology to investigate the dynamic behavior of the slab with and without side crack. Firstly, the dynamic substructure of the finite element model of the SCS is obtained by eigenvalue analysis. Secondly, the SCS model has been applied to the classical vehicle-track coupled dynamic system, and the coupled dynamic model of the train-side cracked slab (TSCS) is established, which considers the multi-rigid-body vehicle subsystem, the flexible rail subsystem, the flexible SCS and subgrade foundation subsystem, and the wheel-rail nonlinear contact model. Later, the accuracy and reliability of the proposed model are verified by the comparison with field tests. Finally, the differences in the vibration characteristics of the vehicle and track structure with and without a side crack are compared and analyzed, and its dynamic stress of the slab as variable train speeds is revealed. The results indicate that the existence of a side crack will reduce the natural frequency of the slab, with a maximum decrease of 22.31%. Although the SCS has a minor effect on the vehicle responses, the existence of a side crack has a significant impact on the slab, especially on the root mean square (RMS) of the vibration acceleration of the slab, with a maximum difference of 74.10% laterally and an increase of 55.16% vertically. Meanwhile, the train speed remarkably affects the crack tip dynamic stress of the SCS, with a maximum increase of 76.07% in the longitudinal direction, 50.34% in the lateral direction and 49.09% in the vertical direction. Higher train speeds will further exacerbate crack propagation under long-term train loading. This study may contribute to the dynamic modeling of TSCS systems and the maintenance of the slab ballastless track.

The effect of transepithelial corneal collagen cross-linking treatment on optical quality of the cornea in keratoconus: 12-month clinical results.

To evaluate the effect of transepithelial corneal collagen crosslinking (CXL) treatment on the optical performance of the cornea at 12-month follow-up after CXL in patients with progressive keratoconus. One hundred and ten eyes of 67 patients were included. The following corneal optical aberrations over the 4-mm-diameter pupil were recorded via Sirius dual-scanning corneal tomography: total, anterior and posterior amount of corneal higher order aberrations [HOAs], vertical coma, horizontal coma, vertical trefoil, oblique trefoil, and spherical aberration, and Strehl ratio of point spread function (PSF). There were significant improvements in mean root mean square error values for corneal total HOA, total coma, anterior HOA, anterior coma, and vertical coma following CXL (P > 0.05, for all). No significant changes were found in the posterior aberometric parameters.PSF value did not change after CXL (P > 0.05). The corneal topographic measurements not revealed a change in the mean simulated keratometry-1, simulated keratometry-2, and maximum keratometry compared with the baseline measurements (P > 0.05, for all). At 12months, there was a significant improvement in the uncorrected (UCVA) and best corrected (BCVA) visual acuity (P < 0.001, both). Most corneal aberrations correlated significantly with postoperative BCVA, but changes in HOAs were not statistically associated with improvements in visual acuity. Transepithelial CXL was effective in stabilizing the keratometric indices and improving the most corneal aberrations in keratoconic eyes 1year after the procedure. While the healing effect on aberrations after CXL was in total and anterior parameters, no significant changes were observed in the posterior surface. In addition, it was observed that transepithelial CXL treatment did not cause a significant change in PSF distribution data.